Showing papers by "Beijing University of Technology published in 2019"

Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma

Abstract: Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia1. Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50–70%2. Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)—high expression of which is a signature specific to the S-III subtype—alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it. A subtype of early-stage hepatocellular carcinoma characterized by disrupted cholesterol homeostasis and associated with a poor prognosis responds to treatment with the SOAT1 inhibitor avasimibe in a patient-derived xenograft mouse model.

Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges

Abstract: Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored.

Strain engineering in perovskite solar cells and its impacts on carrier dynamics.

Abstract: The mixed halide perovskites have emerged as outstanding light absorbers for efficient solar cells. Unfortunately, it reveals inhomogeneity in these polycrystalline films due to composition separation, which leads to local lattice mismatches and emergent residual strains consequently. Thus far, the understanding of these residual strains and their effects on photovoltaic device performance is absent. Herein we study the evolution of residual strain over the films by depth-dependent grazing incident X-ray diffraction measurements. We identify the gradient distribution of in-plane strain component perpendicular to the substrate. Moreover, we reveal its impacts on the carrier dynamics over corresponding solar cells, which is stemmed from the strain induced energy bands bending of the perovskite absorber as indicated by first-principles calculations. Eventually, we modulate the status of residual strains in a controllable manner, which leads to enhanced PCEs up to 20.7% (certified) in devices via rational strain engineering. The residual strains in the mixed halide perovskite thin films and their effects on the solar cell devices are less understood. Here Zhu et al. study the impact of the gradient in-plane strain on the carrier dynamics of the strained perovskite films and optimize the device efficiency.

Boosting oxygen evolution of single-atomic ruthenium through electronic coupling with cobalt-iron layered double hydroxides

Abstract: Single atom catalyst, which contains isolated metal atoms singly dispersed on supports, has great potential for achieving high activity and selectivity in hetero-catalysis and electrocatalysis. However, the activity and stability of single atoms and their interaction with support still remains a mystery. Here we show a stable single atomic ruthenium catalyst anchoring on the surface of cobalt iron layered double hydroxides, which possesses a strong electronic coupling between ruthenium and layered double hydroxides. With 0.45 wt.% ruthenium loading, the catalyst exhibits outstanding activity with overpotential 198 mV at the current density of 10 mA cm−2 and a small Tafel slope of 39 mV dec−1 for oxygen evolution reaction. By using operando X-ray absorption spectroscopy, it is disclosed that the isolated single atom ruthenium was kept under the oxidation states of 4+ even at high overpotential due to synergetic electron coupling, which endow exceptional electrocatalytic activity and stability simultaneously. While water splitting offers a carbon-neutral means to store energy, water oxidation is sluggish and corrosive over earth-abundant electrocatalysts. Here, authors show single ruthenium atoms over cobalt-iron layered double hydroxides to be effective and stable oxygen evolution electrocatalysts.

Bismuth Single Atoms Resulting from Transformation of Metal-Organic Frameworks and Their Use as Electrocatalysts for CO 2 Reduction

Abstract: The electrocatalytic reduction reaction of CO2 (CO2RR) is a promising strategy to promote the global carbon balance and combat global climate change. Herein, exclusive Bi-N4 sites on porous carbon ...

Metal-Organic Frameworks for Food Safety.

Abstract: Food safety is a prevalent concern around the world. As such, detection, removal, and control of risks and hazardous substances present from harvest to consumption will always be necessary. Metal-organic frameworks (MOFs), a class of functional materials, possess unique physical and chemical properties, demonstrating promise in food safety applications. In this review, the synthesis and porosity of MOFs are first introduced by some representative examples that pertain to the field of food safety. Following that, the application of MOFs and MOF-based materials in food safety monitoring, food processing, covering preservation, sanitation, and packaging is overviewed. Future perspectives, as well as potential opportunities and challenges faced by MOFs in this field will also be discussed. This review aims to promote the development and progress of MOF chemistry and application research in the field of food safety, potentially leading to novel solutions.

Atomically dispersed nickel as coke-resistant active sites for methane dry reforming

Abstract: Dry reforming of methane (DRM) is an attractive route to utilize CO2 as a chemical feedstock with which to convert CH4 into valuable syngas and simultaneously mitigate both greenhouse gases. Ni-based DRM catalysts are promising due to their high activity and low cost, but suffer from poor stability due to coke formation which has hindered their commercialization. Herein, we report that atomically dispersed Ni single atoms, stabilized by interaction with Ce-doped hydroxyapatite, are highly active and coke-resistant catalytic sites for DRM. Experimental and computational studies reveal that isolated Ni atoms are intrinsically coke-resistant due to their unique ability to only activate the first C-H bond in CH4, thus avoiding methane deep decomposition into carbon. This discovery offers new opportunities to develop large-scale DRM processes using earth abundant catalysts.

Highly efficient p-type Cu3P/n-type g-C3N4 photocatalyst through Z-scheme charge transfer route

Abstract: Phosphides exhibit relatively low overpotential for electrical hydrogen evolution reaction (HER), thus they have great potential to be used for cocatalyst for photocatalyst. Cu3P, as a p-type semiconductor, tends to form a p-n junction with an n-type photocatalyst. Typically, it is treated as a sensitizer to extend the light absorption. However, its function and work mechanism are not fully understood in the catalyst system. In this report, we synthesized g-C3N4 and loaded Cu3P nanoparticle on its surface. The photoluminescence (PL) spectra, photocurrent and electrochemical impedance spectra confirm the Cu3P greatly enhance the charge separation process. Electrochemical HER results indicate that the composites have lower over-potential for HER. These results confirm the Cu3P works as a cocatalyst in the system, not a sensitizer. Further, we tracked the photogenerated electron transfer direction via photodeposition of Pt nanoparticles. The Pt nanoparticles tend to deposit near the Cu3P nanoparticles. That illustrates the photogenerated electron will be left on Cu3P nanoparticles. On the other hand, the photocatalytic decomposition of Rhodamine B (RhB) illustrates that the holes are left on the g-C3N4 due to both g-C3N4 and Cu3P/g-C3N4 have similar decomposition rate, but the Cu3P cannot decompose RhB. Based on these, we proposed the photogenerated electron of g-C3N4 recombine with the hole of Cu3P, the photogenerated electron of Cu3P will be left for HER. That reasonably explain the cocatalyst function of Cu3P in the composite catalyst system.

Charge-transfer electronic states in organic solar cells

Abstract: In organic solar cells, the charge-transfer (CT) electronic states that form at the interface between the electron-donor (D) and electron-acceptor (A) materials have a crucial role in exciton-dissociation, charge-separation and charge-recombination processes. Since the introduction of active layers consisting of D–A bulk heterojunctions, CT states have been the focus of extensive experimental and theoretical studies. In this Review, we assess the current understanding of CT states and describe how factors such as the geometry of the D–A interface, electronic polarization and the extent of electron delocalization affect their nature and influence the radiative and non-radiative decay processes. We focus on the description and application of fundamental concepts, which provides the framework to discuss the path to organic solar cells with efficiencies comparable to those in inorganic photovoltaic technologies. The charge-transfer electronic states that form at the interfaces between electron-donor and electron-acceptor components have a key role in the electronic processes in organic solar cells. This Review describes the current understanding of how these charge-transfer states affect device performance.

Resource Trading in Blockchain-Based Industrial Internet of Things

Abstract: Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

De Novo Design of Chemical Stability Near-Infrared Molecular Probes for High-Fidelity Hepatotoxicity Evaluation In Vivo.

Abstract: Near-infrared (NIR) fluorescence imaging technique is garnering increasing research attention due to various advantages. However, most NIR fluorescent probes still suffer from a false signals problem owing to their instability in real application. Especially in a pathological environment, many NIR probes can be easily destroyed due to the excessive generation of highly reactive species and causing a distorted false signal. Herein, we proposed an approach for developing a new stable NIR dye platform with an optically tunable group to eliminate false signals using the combination of dyes screening and rational design strategy. The conception is validated by the construction of two high-fidelity NIR fluorescent probes (NIR-LAP and NIR-ONOO–) sensing leucine aminopeptidase (LAP) and peroxynitrite (ONOO–), the markers of hepatotoxicity. These probes (NIR-LAP and NIR-ONOO–) were demonstrated to sensitively and accurately monitor LAP and ONOO– (detection limit: 80 mU/L for LAP and 90 nM for ONOO–), thereby allow...

Unprecedented Eighteen-Faceted BiOCl with a Ternary Facet Junction Boosting Cascade Charge Flow and Photo-redox.

Abstract: Exposure of anisotropic crystal facets allows the directional transfer of photoexcited electrons (e- ) and holes (h+ ), for spatial charge separation. High-index facets with a high density of low-coordinated atoms always serve as reactive catalytic sites. However, preparation of multi-facets or high-index facets is highly challenging for layered bismuth-based photocatalysts. Herein, we report the preparation of unprecedented eighteen-faceted BiOCl with {001} top facets and {102} and {112} oblique facets via a hydrothermal process. Compared to the conventional BiOCl square plates with {001} top facets and {110} lateral facets, the eighteen-faceted BiOCl has highly enhanced photocatalytic activity for H2 evolution and hydroxyl radicals (. OH) production. Theoretical calculations and photodeposition results disclose that the of eighteen-faceted BiOCl has a well-matched {001}/{102}/{112} ternary facet junction, which provides a cascade path for more efficient charge flow than the binary facet junction in BiOCl square plates.

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Abstract: In this paper we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g. object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g. visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images which are generally thought to be of the best quality. In this work, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measre of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image datasets. Results of experiments on nine datasets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-, reduced- and no-reference IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images and dehazed images. The source code will be released at this https URL.

Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network

Abstract: A recurrent neural network (RNN) called CrackNet‐R is proposed in the article for fully automated pixel‐level crack detection on three‐dimensional (3D) asphalt pavement surfaces. In the ar...

General π-Electron-Assisted Strategy for Ir, Pt, Ru, Pd, Fe, Ni Single-Atom Electrocatalysts with Bifunctional Active Sites for Highly Efficient Water Splitting

Abstract: Both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) are crucial to water splitting, but require alternative active sites. Now, a general π-electron-assisted strategy to anchor single-atom sites (M=Ir, Pt, Ru, Pd, Fe, Ni) on a heterogeneous support is reported. The M atoms can simultaneously anchor on two distinct domains of the hybrid support, four-fold N/C atoms (M@NC), and centers of Co octahedra (M@Co), which are expected to serve as bifunctional electrocatalysts towards the HER and the OER. The Ir catalyst exhibits the best water-splitting performance, showing a low applied potential of 1.603 V to achieve 10 mA cm in 1.0 m KOH solution with cycling over 5 h. DFT calculations indicate that the Ir@Co (Ir) sites can accelerate the OER, while the Ir@NC sites are responsible for the enhanced HER, clarifying the unprecedented performance of this bifunctional catalyst towards full water splitting.

A Multi-View Deep Learning Framework for EEG Seizure Detection

Abstract: The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual inspection from long-term EEG readings, automatic EEG seizure detection has garnered increasing attention among researchers. In this paper, we present a unified multi-view deep learning framework to capture brain abnormalities associated with seizures based on multi-channel scalp EEG signals. The proposed approach is an end-to-end model that is able to jointly learn multi-view features from both unsupervised multi-channel EEG reconstruction and supervised seizure detection via spectrogram representation. We construct a new autoencoder-based multi-view learning model by incorporating both inter and intra correlations of EEG channels to unleash the power of multi-channel information. By adding a channel-wise competition mechanism in the training phase, we propose a channel-aware seizure detection module to guide our multi-view structure to focus on important and relevant EEG channels. To validate the effectiveness of the proposed framework, extensive experiments against nine baselines, including both traditional handcrafted feature extraction and conventional deep learning methods, are carried out on a benchmark scalp EEG dataset. Experimental results show that the proposed model is able to achieve higher average accuracy and f1-score at 94.37% and 85.34%, respectively, using 5-fold subject-independent cross validation, demonstrating a powerful and effective method in the task of EEG seizure detection.

A pigeon-inspired optimization algorithm for many-objective optimization problems

Abstract: 1Complex System and Computational Intelligence Laboratory, TaiYuan University of Science and Technology, Taiyuan 003024, China; 2Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; 3State Key Laboratory of Intelligent Con-trol and Management of Complex Systems, Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China.; 4University of Technology Sydney, Sydney, NSW 2007, Australia

A stable zirconium based metal-organic framework for specific recognition of representative polychlorinated dibenzo-p-dioxin molecules.

Abstract: Polychlorinated dibenzo-p-dioxins (PCDDs), as a class of persistent and highly toxic organic pollutants, have been posing a great threat to human health and the environment. The sensing of these compounds is important but challenging. Here, we report a highly stable zirconium-based metal-organic framework (MOF), Zr6O4(OH)8(HCOO)2(CPTTA)2 (BUT-17) with one-dimensional hexagonal channels and phenyl-rich pore surfaces for the recognition and sensing of two representative PCDDs, 2,3-dichlorodibenzo-p-dioxin (BCDD) and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), based on the fluorescence quenching. BUT-17 exhibits high sensing ability with the detection limits as low as 27 and 57 part per billion toward BCDD and TCDD, respectively, and is very selective as well without the interference of similar compounds. The recognition of BUT-17 toward BCDD is demonstrated by single-crystal structure of its guest-loaded phase, in which the fluorescence-quenched complexes form between the adsorbed BCDD molecules and the MOF host through π-π stacking and hydrogen bonding interactions.

A data-driven lane-changing model based on deep learning

Abstract: Lane-changing (LC), which is one of the basic driving behavior, largely impacts on traffic efficiency and safety. Modeling an LC process is challenging due to the complexity and uncertainty of driving behavior. To address this issue, this paper proposes a data-driven LC model based on deep learning models. Deep belief network (DBN) and long short-term memory (LSTM) neural network are employed to model the LC process that is composed of LC decisions (LCD) and LC implementation (LCI). The empirical LC data provided by Next Generation Simulation project (NGSIM) is utilized to train and test the proposed DBN-based LCD model and LSTM-based LCI model. The results indicate that the proposed data-driven model is able to accurately predict the LC process of a vehicle. The sensitivity analysis shows that the most important factor associated with LCD is the relative position of the preceding vehicle in the target lane. This may be the first work that comprehensively models LC using deep learning approaches.